A fundamental step in image interpretation is pattern recognition, which essentially involves the process of analyzing one or more pixels of a given image and assigning one or more pixels to one of a limited number of pre-defined categories, or classes, based on the value(s) of the one or more pixels. One or more of the pre-defined categories are the patterns, or features, to be recognized and extracted. As is known in the art, the algorithm to determine which category to assign a pixel of an image may be established by providing a generic computational procedure a large number of sample images for each category and having the computational procedure determine the characteristics for each category that are unique compared to the other categories, such as color or brightness.
The accuracy of this approach is dependent upon the effectiveness of the determined unique characteristics. For example, turning to FIG. 1a, an image is shown having a generally circular region 10 of gray points in the center of the image. In one pattern recognition system, it may be desirable to identify and locate this circular region 10 in the image. To develop such a system, small regions of pixels are evaluated throughout the picture. By evaluating the values and/or patterns of certain characteristics, such as brightness or color, of each pixel, or regions of pixels, and mapping or graphing the values, unique characteristics may become apparent. For example, turning to FIG. 1d, the brightness of each region of pixels is evaluated, and a mean value of brightness for each region of pixels is calculated along with a corresponding standard deviation and graphed according to its mean and standard deviation. From such a graphing, two groups become apparent, regions of pixels 14 associated with areas of the image within the circular region 10 and regions of pixels 16 associated with areas of the image outside the circular region 10. From this information, pre-defined categories may be established, and the pattern recognition algorithm may be configured to evaluate regions of pixels, assign them to the appropriate categories, and extract the desired patterns or features.
However, often times, imaging systems may introduce imperfections, such as blurring, into the images they produce, and thus, may generate images such as that shown in FIG. 1b instead of that shown in FIG. 1a. The desired pattern, shown in the circular region 10 of FIG. 1a, cannot be visually detected in FIG. 1b. A pattern recognition system that can detect a desired pattern from such an image would be desirable.